library(readr)
crimenes_onu <- read_csv("Base_de_datos/Crimes_UN_data.csv")
Missing column names filled in: 'X3' [3], 'X4' [4], 'X5' [5], 'X6' [6], 'X7' [7]
-- Column specification -------------------------------------------------------------------
cols(
T12 = col_character(),
`Intentional homicides and other crimes` = col_character(),
X3 = col_character(),
X4 = col_character(),
X5 = col_character(),
X6 = col_character(),
X7 = col_character()
)
migracion_de_mexico <- read_csv("Base_de_datos/Encuesta_Nacional_de_Migracion.csv")
-- Column specification -------------------------------------------------------------------
cols(
.default = col_double(),
D_R = col_logical(),
ageb = col_character(),
p4_1 = col_character(),
p4_2 = col_character(),
p4_3 = col_character(),
p8 = col_character(),
p11 = col_character(),
p12 = col_character(),
p29 = col_character(),
p30 = col_character(),
p41 = col_character(),
p69b = col_character(),
p69ba = col_character(),
obser = col_character()
)
i Use `spec()` for the full column specifications.
poblacion_mundial_y_migrantes <- read_csv("Base_de_datos/Total_de_la_poblacion_mundial_y_migrante_segun_sexo_y_nivel_de_desarrollo_regional_1960_2015_Base.csv")
Missing column names filled in: 'X14' [14], 'X15' [15], 'X16' [16], 'X17' [17], 'X18' [18], 'X19' [19], 'X20' [20], 'X21' [21], 'X22' [22]
-- Column specification -------------------------------------------------------------------
cols(
.default = col_logical(),
`A<U+663C><U+3E31>o` = col_double(),
Poblacion_mundial = col_character(),
Total_migrantes = col_character(),
Migrantes_Hombres = col_character(),
Migrantes_Mujeres = col_character(),
Regiones_mas_desarrolladas = col_character(),
Regiones_menos_desarolladas = col_character(),
`Tasa_poblaci<U+663C><U+3E33>n_mundial` = col_double(),
Tasa_Total_migrantes = col_double(),
Tasa_Migrantes_Hombres = col_double(),
Tasa_Migrantes_Mujeres = col_double(),
Tasa_regiones_mas_desarrolladas = col_double(),
Tasa_Regiones_menos_desarolladas = col_double()
)
i Use `spec()` for the full column specifications.
revisiones_migratorias <-read_csv("Base_de_datos/revisiones-migratorias.csv")
-- Column specification -------------------------------------------------------------------
cols(
.default = col_double(),
Entidad = col_character()
)
i Use `spec()` for the full column specifications.
Percepcion_Seguridad_Publica_Datos <- read_delim("Base_de_datos/Percepcion Seguridad Publica - Datos.csv",
";", escape_double = FALSE, trim_ws = TRUE)
-- Column specification -------------------------------------------------------------------
cols(
Entidad = col_character(),
`2010` = col_double(),
`2011` = col_double(),
`2012` = col_double(),
`2013` = col_double(),
`2014` = col_double(),
`2015` = col_double(),
`2016` = col_double(),
`2017` = col_double(),
`2018` = col_double()
)
Tabla de inmigración en México
migracion_de_mexico_clean = migracion_de_mexico[,c(54:57,87:90,142,149:150,152:171,179:180,182:183,194:195,210:211,213:215,219:220,222:233,235,250,253:260)]
migracion_de_mexico_clean <- na.omit(migracion_de_mexico_clean)
migracion_de_mexico_clean
migracion_de_mexico
y = table(migracion_de_mexico_clean$total2)
y
1
1198
max(y)
[1] 1198
factor.ageb <- factor(migracion_de_mexico$ageb)
factor.p4_1 <- factor(migracion_de_mexico$p4_1)
levels(factor.p4_1)
levels(factor.migracion_de_mexico)
table(migracion_de_mexico$hr_ini1)
table(migracion_de_mexico$loca)
revisiones_migratorias_clean <- na.omit(revisiones_migratorias)
I_2015 <- data.frame(I_2015 = revisiones_migratorias_clean$`2015-01` + revisiones_migratorias_clean$`2015-02` + revisiones_migratorias_clean$`2015-03`)
II_2015 <- data.frame(II_2015 = revisiones_migratorias_clean$`2015-04` + revisiones_migratorias_clean$`2015-05` + revisiones_migratorias_clean$`2015-06`)
III_2015 <- data.frame(III_2015 = revisiones_migratorias_clean$`2015-07` + revisiones_migratorias_clean$`2015-08` + revisiones_migratorias_clean$`2015-09`)
IV_2015 <- data.frame(IV_2015 = revisiones_migratorias_clean$`2015-10` + revisiones_migratorias_clean$`2015-11` + revisiones_migratorias_clean$`2015-12`)
I_2016 <- data.frame(I_2016 = revisiones_migratorias_clean$`2016-01` + revisiones_migratorias_clean$`2016-02` + revisiones_migratorias_clean$`2016-03`)
II_2016 <- data.frame(II_2016 = revisiones_migratorias_clean$`2016-04` + revisiones_migratorias_clean$`2016-05` + revisiones_migratorias_clean$`2016-06`)
III_2016 <- data.frame(III_2016 = revisiones_migratorias_clean$`2016-07` + revisiones_migratorias_clean$`2016-8` + revisiones_migratorias_clean$`2016-09`)
IV_2016 <- data.frame(IV_2016 = revisiones_migratorias_clean$`2016-10` + revisiones_migratorias_clean$`2016-11` + revisiones_migratorias_clean$`2016-12`)
I_2017 <- data.frame(I_2017 = revisiones_migratorias_clean$`2017-01` + revisiones_migratorias_clean$`2017-02` + revisiones_migratorias_clean$`2017-03`)
II_2017 <- data.frame(II_2017 = revisiones_migratorias_clean$`2017-04` + revisiones_migratorias_clean$`2017-05` + revisiones_migratorias_clean$`2017-06`)
III_2017 <- data.frame(III_2017 = revisiones_migratorias_clean$`2017-07` + revisiones_migratorias_clean$`2017-08` + revisiones_migratorias_clean$`2017-9`)
IV_2017 <- data.frame(IV_2017 = revisiones_migratorias_clean$`2017-10` + revisiones_migratorias_clean$`2017-11` + revisiones_migratorias_clean$`2017-12`)
I_2018 <- data.frame(I_2018 = revisiones_migratorias_clean$`2018-01` + revisiones_migratorias_clean$`2018-02` + revisiones_migratorias_clean$`2018-03`)
II_2018 <- data.frame(II_2018 = revisiones_migratorias_clean$`2018-04` + revisiones_migratorias_clean$`2018-05` + revisiones_migratorias_clean$`2018-06`)
III_2018 <- data.frame(III_2018 = revisiones_migratorias_clean$`2018-07` + revisiones_migratorias_clean$`2018-08` + revisiones_migratorias_clean$`2018-09`)
IV_2018 <- data.frame(IV_2018 = revisiones_migratorias_clean$`2018-10` + revisiones_migratorias_clean$`2018-11` + revisiones_migratorias_clean$`2018-12`)
Nueva_tabla <- data.frame(c(revisiones_migratorias[,2], I_2015, II_2015, III_2015, IV_2015, I_2016, II_2016, III_2016, IV_2016, I_2017, II_2017, III_2017, IV_2017, I_2018, II_2018, III_2018, IV_2018))
revisiones_migratorias_clean
Nueva_tabla
na.omit(Percepcion_Seguridad_Publica_Datos)
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